Langchain vs RAG vs LLM

Artificial intelligence (AI) is transforming how we build intelligent systems. You’ve probably heard terms like Langchain, RAG, and LLM. But which one should you use? Let’s break it down.

Why Does This Matter?

When building AI applications, you need the right tool for the job. LLM, RAG, and Langchain all serve different purposes. Using the right one can make your system more powerful. Let’s explore when to use each one.

What is LLM in Artificial Intelligence ?

An LLM (Large Language Model) is a powerful tool for generating text. These models, like GPT-3, understand language and generate responses based on what they’ve learned. They can:

• Answer questions.
• Generate text.
• Summarize articles.

However, LLMs are limited by the knowledge they were trained on. They don’t have access to real-time information however some LLMs are having serach features now.

Here’s a simple example:
you can find a great golang package to start building LLM apps

Python Code Example:

import openai  

openai.api_key = 'api-key'  
response = openai.Completion.create(  
    engine="text-davinci-003",  
    prompt="What is artificial intelligence?",  
    max_tokens=50  
)  
print(response.choices[0].text.strip())  

In this case, the LLM answers the question based on its training, not live data. you can find a recent website which has some tools built on top of LLM: https://www.modcode.dev/

Artificial intelligence : What is RAG?

RAG (Retrieval-Augmented Generation) takes LLMs a step further. It retrieves real-time data before generating text. This is useful when your AI needs up-to-date information.

For example, imagine building a chatbot. With RAG, your chatbot can pull data from your website before answering a customer’s question, like, “What’s the latest sale?”

What is Langchain?

Langchain is a framework that lets you connect LLMs, APIs, and databases. It’s perfect for more complex tasks.

If you need to build a multi-step AI workflow, Langchain can handle it. For instance, Langchain can combine data from various sources, pass it to an LLM, and then generate a comprehensive answer.

Python Code Example for Langchain:

from langchain.chains import LLMChain  
from langchain.prompts import PromptTemplate  

template = "Explain {topic} in simple terms."  
prompt = PromptTemplate(input_variables=["topic"], template=template)  
llm_chain = LLMChain(prompt=prompt)  

result = llm_chain.run("artificial intelligence")  
print(result)  

In this case, Langchain allows the model to generate a detailed response based on the input topic.

When to Use LLM, RAG, or Langchain?

• LLM: Use it when you need to generate text based on general knowledge.
• RAG: Use it when you need to pull up-to-date or domain-specific information.
• Langchain: Use it when you need to combine multiple tools or systems into one workflow.

Use Cases

• LLM: You need a chatbot to answer general questions like “What is artificial intelligence?”
• RAG: Your chatbot needs to pull data from a live product catalog or promotions page.
• Langchain: You need to query a database, analyze the results with an LLM, and generate a custom report.

LLM, RAG, and Langchain each have their own strengths. Use LLM for text generation, RAG for live data, and Langchain for complex workflows. Knowing when to use each one will make your AI system more powerful. the best choice is always based on waht you want to achieve 🙂